arXiv AI
Aug 5
Risky Business: Measuring The Faithfulness-Safety Tension
arXiv:2608. 03745v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning offers a promising window into model monitoring.
Towards Understanding the Cognitive Habits of Large Reasoning Models
arXiv:2506. 21571v3 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs), which autonomously produce a reasoning Chain of Thought (CoT) before producing final responses, offer a promising approach to interpreting and monitoring model behaviors.
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arXiv AI
Aug 3
On the Generalization of Steering Vectors for Chain-of-Thought Faithfulness
arXiv:2607. 29062v1 Announce Type: new Abstract: Model capabilities have improved in large part due to scaling chain of thought.
arXiv AI
Jun 2
An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models
arXiv:2606. 01462v1 Announce Type: new Abstract: Studies of human reasoning have shown that people are typically stronger at evaluating reasoning than producing it from scratch.
arXiv AI
2d ago
Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models
arXiv:2608. 13760v1 Announce Type: cross Abstract: Which reasoning behaviors are associated with correct answers in reasoning models, and does reasoning-oriented training amplify those behaviors?
arXiv AI
Aug 11
Understanding and Mitigating Premature Confidence for Better LLM Reasoning
arXiv:2605. 24396v2 Announce Type: replace Abstract: Long chains of thought (CoT) from current language models frequently contain logical gaps and unjustified leaps, limiting the gains from additional test-time compute.
arXiv AI
Aug 10
Counterfactual Simulation Training for Chain-of-Thought Faithfulness
arXiv:2602. 20710v2 Announce Type: replace Abstract: Inspecting Chain-of-Thought reasoning is among the most common means of understanding why an LLM produced its output.
arXiv AI
Jun 26
Humans Disengage, Reasoning Models Persist: Separating Difficulty Registration from Deliberation Allocation
arXiv:2606. 26502v1 Announce Type: new Abstract: Large reasoning models (LRMs) take longer on harder problems, just as humans do.
arXiv AI
Jun 2
Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs
arXiv:2606. 00726v1 Announce Type: new Abstract: Strong reasoning depends not only on model knowledge but also on how effectively cognitive behaviors are deployed during generation.
arXiv AI
Jun 12
Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning
arXiv:2606. 13607v1 Announce Type: new Abstract: When large language models (LLMs) fail to generalize or make haphazard errors in reasoning, it is often taken as evidence that LLMs are not truly reasoning, but rather performing a kind of pattern matching.
arXiv AI
Jun 3
Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models
arXiv:2606. 02835v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning is consistently beneficial remains under-examined.
arXiv Machine Learning
Jun 4
Reasoning Shift: How Context Silently Shortens LLM Reasoning
arXiv:2604. 01161v2 Announce Type: replace Abstract: Large language models (LLMs) exhibiting test-time scaling behavior, such as extended reasoning traces and self-verification, have demonstrated remarkable performance on complex, long-term reasoning tasks.
arXiv Machine Learning
Jun 30
NeuReasoner: Theory-grounded Mapping of Reasoning Elicitation Boundaries
arXiv:2606. 29971v1 Announce Type: new Abstract: A growing body of work suggests that the reasoning capabilities of large language models are largely latent in their base form, with post-training primarily amplifying rather than introducing them.